Speech technology and colorization for audiovisual archives

[This post describes and is based on Rudy Marsman‘s MSc thesis and is partly based on a Dutch blog post by him]

The Netherlands Institute for Sound and Vision (NISV) archives Dutch broadcast TV and makes it available to researchers, professionals and the general public. One subset are the Polygoonjournaals (Public News broadcasts) that are published under open licenses as part of the OpenImages platform. NISV is also interested in exploring new ways and technologies to make interaction with the material easier and to increase exposure to their archives. In this context, Rudy explored two options.

Two stills from the film ‘Steegjes‘, with the right frame colorized. Source: Polygoon-Profilti (producent) / Nederlands Instituut voor Beeld en Geluid  / colorized by Rudy Marsman, CC BY-SA

One part of the research was the autonomous colorization of old black-and-white video footage using Neural Networks. Rudy used a pre-trained NN (Zhang et al 2016) that is able to colorize black and white images. Rudy developed a program to split videos into frames, colorize the individual frames using the NN and then ‘stitch’ them back together into colorized videos. The stunning results were very well received by NISV employees. Examples are shown below.

Tour de France 1954 (colorized by Rudy Marsman in 2016), Polygoon-Profilti (producent) / Nederlands Instituut voor Beeld en Geluid (beheerder), CC-BY SA

Results from the comparison of the different variants of the method on different corpora
Results from the comparison of the different variants of the method on different corpora

In the other part of his research, Rudy investigated to what extent the existing news broadcast corpus, with a voice-overs from the famous Philip Bloemendal  can be used to develop a modern text-to-speech engine with his voice. To do so he have mainly focused on natural language processing and the determination to what extent the language used by Bloemendal in the 1970s is still comparable enough to contemporary Dutch.

Rudy used precompiled automatic speech recognition (ASR) results to match words to sounds and developed a slot-and-filler text-to-speech system based on this. To increase the limited vocabulary, he implemented a number of strategies, including term-expansion through the use of Open Dutch Wordnet and smart decompounding (this mostly works for Dutch, mapping ‘sinterklaasoptocht’ to ‘sinterklaas’ and ‘optocht’. The different strategies were compared to a baseline. Rudy found that a combination of the two resulted in the best performance (see figure). For more information:

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Crowd- and nichesourcing for film and media scholars

[This post describes Aschwin Stacia‘s MSc. project and is based on his thesis]

There are many online and private film collections that lack structured annotations to facilitate retrieval. In his Master project work, Aschwin Stacia explored the effectiveness of a crowd-and nichesourced film tagging platform,  around a subset of the Eye Open Beelden film collection.

Specifically, the project aimed at soliciting annotations appropriate for various types of media scholars who each have their own information needs. Based on previous research and interviews, a framework categorizing these needs was developed. Based on this framework a data model was developed that matches the needs for provenance and trust of user-provided metadata.

Fimtagging screenshot
Screenshot of the FilmTagging tool, showing how users can annotate a video

A crowdsourcing and retrieval platform (FilmTagging) was developed based on this framework and data model. The frontend of the platform allows users to self-declare knowledge levels in different aspects of film and also annotate (describe) films. They can also use the provided tags and provenance information for retrieval and extract this data from the platform.

To test the effectiveness of platform Aschwin conducted an experiment in which 37 participants used the platform to make annotations (in total, 319 such annotations were made). The figure below shows the average self-reported knowledge levels.

Average self-reported knowledge levels on a 5-point scale. The topics are defined by the framework, based on previous research and interviews.
Average self-reported knowledge levels on a 5-point scale. The topics are defined by the framework, based on previous research and interviews.

The annotations and the platform were then positively evaluated by media scholars as it could provide them with annotations that directly lead to film fragments that are useful for their research activities.

Nevertheless, capturing every scholar’s specific information needs is hard since the needs vary heavily depending on the research questions these scholars have.

  • Read more details in Aschwin’s thesis [pdf].
  • Have a look at the software at https://github.com/Aschwinx/Filmtagging , and maybe start your own Filmtagging instance
  • Test the annotation platform yourself at http://astacia.eculture.labs.vu.nl/ or watch the screencast below

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